Bottom Line:
An accurate estimation of sample size needed for the desired purpose of an experiment will be important for effort-efficiency and cost-effectiveness.The proposed GVL score integrates the information of CNA region, the number of abnormal chromosomes and the total number of the altered SNPs at the genotype level.Statistical tests indicate that the high and low grade MDS patients can be well separated by GVL score, which appears to correlate better with clinical outcome than the traditional classification approaches using morphology and IPSS sore at the phenotype level.

ABSTRACTCopy Number Aberration (CNA) in myelodysplastic syndromes (MDS) study using single nucleotide polymorphism (SNP) arrays have been received increasingly attentions in the recent years. In the current study, a new Constraint Moving Average (CMA) algorithm is adopted to determine the regions of CNA regions first. In addition to large regions of CNA, using the proposed CMA algorithm, small regions of CNA can also be detected. Real-time Polymerase Chain Reaction (qPCR) results prove that the CMA algorithm presents an insightful discovery of both large and subtle regions. Based on the results of CMA, two independent applications are studied. The first one is power analysis for sample estimation. An accurate estimation of sample size needed for the desired purpose of an experiment will be important for effort-efficiency and cost-effectiveness. The power analysis is performed to determine the minimum sample size required for ensuring at least (0Show MeSH

pone-0005054-g001: Illustration of the CMA algorithm.MDS-2 Lymphoid is the reference, and MDS-2 Erythroid is the test sample. In the test sample, the average log-2 intensities of every five consecutive SNPs in the circled region are higher than 0.35, with the small SDs (< = 0.15). Selected overlapping regions are merged into a large region.

Mentions:
The threshold of log-2 ratio here is ±0.35, the same as in [17]. Finally, all the selected CNA regions in the samples are recombined in order to exclude the overlapping cases. Figure 1 illustrates the CMA algorithm for MDS-2 Myeloid.

pone-0005054-g001: Illustration of the CMA algorithm.MDS-2 Lymphoid is the reference, and MDS-2 Erythroid is the test sample. In the test sample, the average log-2 intensities of every five consecutive SNPs in the circled region are higher than 0.35, with the small SDs (< = 0.15). Selected overlapping regions are merged into a large region.

Mentions:
The threshold of log-2 ratio here is ±0.35, the same as in [17]. Finally, all the selected CNA regions in the samples are recombined in order to exclude the overlapping cases. Figure 1 illustrates the CMA algorithm for MDS-2 Myeloid.

Bottom Line:
An accurate estimation of sample size needed for the desired purpose of an experiment will be important for effort-efficiency and cost-effectiveness.The proposed GVL score integrates the information of CNA region, the number of abnormal chromosomes and the total number of the altered SNPs at the genotype level.Statistical tests indicate that the high and low grade MDS patients can be well separated by GVL score, which appears to correlate better with clinical outcome than the traditional classification approaches using morphology and IPSS sore at the phenotype level.

ABSTRACTCopy Number Aberration (CNA) in myelodysplastic syndromes (MDS) study using single nucleotide polymorphism (SNP) arrays have been received increasingly attentions in the recent years. In the current study, a new Constraint Moving Average (CMA) algorithm is adopted to determine the regions of CNA regions first. In addition to large regions of CNA, using the proposed CMA algorithm, small regions of CNA can also be detected. Real-time Polymerase Chain Reaction (qPCR) results prove that the CMA algorithm presents an insightful discovery of both large and subtle regions. Based on the results of CMA, two independent applications are studied. The first one is power analysis for sample estimation. An accurate estimation of sample size needed for the desired purpose of an experiment will be important for effort-efficiency and cost-effectiveness. The power analysis is performed to determine the minimum sample size required for ensuring at least (0Show MeSH